Outlier Analysis and Artificial Neural Network for the Noncontact Nondestructive Evaluation of Immersed Plates
Authors: , , Publication: Publication Date: 1 August 2015Testing Method:
We present an experimental study where guided ultrasonic waves were used for the noncontact nondestructive evaluation of an aluminum plate immersed in water. Broadband leaky Lamb waves were generated using a pulsed laser and were detected with an array of immersion transducers arranged in a semicircle. The signals were processed to extract some features from the time, frequency, and joint time-frequency domains. These features were then fed to an unsupervised learning algorithm based on the outlier analysis to detect the presence of damage, and to a supervised learning algorithm based on artificial neural networks to classify the types of defect. We found that the hybrid laser-immersion transducers system and both learning algorithms enable the detection of the defects and their classification with good success rate.
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